fix score if batch_iterator_test doesn't yield same order / size as y

nolearn/lasagne/base.py

@@ -287,7 +287,7 @@ def __init__(
 
  if isinstance(layers, Layer):
  layers = _list([layers])
- 
+
  self.layers = layers
  self.update = update
  self.objective = objective
@@ -686,20 +686,31 @@ def apply_batch_func(func, Xb, yb=None):
  else:
  return func(Xb) if yb is None else func(Xb, yb)
 
- def predict_proba(self, X):
+ def predict_proba(self, X, y=None):
  probas = []
- for Xb, yb in self.batch_iterator_test(X):
+ ys = []
+ for Xb, yb in self.batch_iterator_test(X, y):
  probas.append(self.apply_batch_func(self.predict_iter_, Xb))
- return np.vstack(probas)
+ ys.append(yb)
+ if y is not None:
+ return np.vstack(probas), np.hstack(ys)
+ else:
+ return np.vstack(probas)
 
- def predict(self, X):
+ def predict(self, X, y=None):
  if self.regression:
- return self.predict_proba(X)
+ return self.predict_proba(X, y)
  else:
- y_pred = np.argmax(self.predict_proba(X), axis=1)
+ predictions = self.predict_proba(X, y)
+ if y is not None:
+ predictions, y_actual = predictions
+ y_pred = np.argmax(predictions, axis=1)
  if self.use_label_encoder:
  y_pred = self.enc_.inverse_transform(y_pred)
- return y_pred
+ if y is not None:
+ return y_pred, y_actual
+ else:
+ return y_pred
 
  def get_output(self, layer, X):
  if isinstance(layer, basestring):
@@ -724,7 +735,7 @@ def get_output(self, layer, X):
 
  def score(self, X, y):
  score = mean_squared_error if self.regression else accuracy_score
- return float(score(self.predict(X), y))
+ return float(score(*self.predict(X, y)))
 
  def get_all_layers(self):
  return self.layers_.values()